ArticlePLoS computational biology2020
Nonlinear mixed selectivity supports reliable neural computation.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
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- Explicitly nonlinear fMRI networks reveal hidden trajectories of infant brain development.bioRxiv : the preprint server for biology · 2026Article
- Primate neuroethology: a new synthesis.Trends in cognitive sciences · 2026Review
- From sensory to perceptual manifolds: The twist of neural geometry.Science advances · 2025Article
- Functional specialisation of multisensory temporal integration in the mouse superior colliculus.Nature communications · 2025Article
- Efficient and dynamic neural geometry of value and modality encoding in the primate putamen for value-guided behavior.Nature communications · 2025Article
- Hand position fields of neurons in the premotor cortex of macaques during natural reaching.Nature communications · 2025Article
- Emergence and maintenance of modularity in neural networks with Hebbian and anti-Hebbian inhibitory STDP.PLoS computational biology · 2025Article
- Asymmetric representation of symmetric semantic information in the human brain.Neuroimage. Reports · 2025Article
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- Nonresponsive Neurons Improve Population Coding of Object Location.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Linking neural population formatting to function.bioRxiv : the preprint server for biology · 2025Article
- Semi-orthogonal subspaces for value mediate a binding and generalization trade-off.Nature neuroscience · 2024Article
- Convergent representation of values from tactile and visual inputs for efficient goal-directed behavior in the primate putamen.Nature communications · 2024Article
- Modular representations emerge in neural networks trained to perform context-dependent tasks.bioRxiv : the preprint server for biology · 2024Article
- A transient high-dimensional geometry affords stable conjunctive subspaces for efficient action selection.Nature communications · 2024Article
- A Transient High-dimensional Geometry Affords Stable Conjunctive Subspaces for Efficient Action Selection.bioRxiv : the preprint server for biology · 2024Article
- The neural basis of swap errors in working memory.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
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3 authors.
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Abstract
Neuronal activity in the brain is variable, yet both perception and behavior are generally reliable. How does the brain achieve this? Here, we show that the conjunctive coding of multiple stimulus features, commonly known as nonlinear mixed selectivity, may be used by the brain to support reliable information transmission using unreliable neurons. Nonlinearly mixed feature representations have been observed throughout primary sensory, decision-making, and motor brain areas. In these areas, different features are almost always nonlinearly mixed to some degree, rather than represented separately or with only additive (linear) mixing, which we refer to as pure selectivity. Mixed selectivity has been previously shown to support flexible linear decoding for complex behavioral tasks. Here, we show that it has another important benefit: in many cases, it makes orders of magnitude fewer decoding errors than pure selectivity even when both forms of selectivity use the same number of spikes. This benefit holds for sensory, motor, and more abstract, cognitive representations. Further, we show experimental evidence that mixed selectivity exists in the brain even when it does not enable behaviorally useful linear decoding. This suggests that nonlinear mixed selectivity may be a general coding scheme exploited by the brain for reliable and efficient neural computation.
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